Neural Network based controller

Summary: In summary, the conversation discusses a project involving temperature control of a room using a neural network that has a 99.5% accuracy rate. The system is highly complex, non-linear, and dynamic, and has 5 inputs (XYZSH) and one output (T). The speaker is seeking suggestions on which control architecture to use and mentions attempting a standard inversion of the system in Matlab, but it was unsuccessful due to fewer outputs than inputs.
  • #1
date.chinmay
10
0
I'm working on a project which deals with temperature control of a room. the idea is to control temperature within a limit.

I have prior data which i used to train a neural network which is 99.5% accurate. there are 5 inputs (x , y , z , A , B) and there is one output (T).

Now I want to use this network in a control architecture of some kind. Any suggestions? which should I use?

P.S. The system is highly complex, non linear and dynamic in nature. I have already tried standard inversion of system but it doesn't work as outputs are lesser than inputs.
I'm doing this in Matlab.
 
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  • #2
What are your 5 inputs?
 
  • #3
3 coordinates of a point.. the fan speed at that point.. and the dissipated heat at that point
XYZ,S,H
 

1. What is a Neural Network based controller?

A Neural Network based controller is a type of artificial intelligence algorithm that uses a network of interconnected nodes to learn and make decisions based on input data. It mimics the way the human brain processes information and can be used for a variety of tasks, such as controlling robots, predicting outcomes, or recognizing patterns.

2. How does a Neural Network based controller work?

A Neural Network based controller is composed of multiple layers of nodes and connections, where each node receives input from the previous layer, processes it, and passes it on to the next layer. Through a process called backpropagation, the network adjusts the strength of its connections based on the desired output, allowing it to learn and make more accurate predictions over time.

3. What are the advantages of using a Neural Network based controller?

One of the main advantages of a Neural Network based controller is its ability to learn and adapt to new situations without being explicitly programmed. It can also handle a large amount of data and complex relationships between inputs and outputs, making it suitable for tasks that involve pattern recognition or prediction.

4. What are some common applications of Neural Network based controllers?

Neural Network based controllers can be used in a variety of fields, including finance, healthcare, transportation, and manufacturing. They are often used for tasks such as image and speech recognition, natural language processing, predictive maintenance, and autonomous driving.

5. Are there any limitations to using a Neural Network based controller?

While Neural Network based controllers have shown impressive capabilities, they are not a one-size-fits-all solution and have some limitations. They require a large amount of data and computing resources to train, and the results can be difficult to interpret. Additionally, they may struggle with new or unexpected inputs, making them less suitable for real-time applications.

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